Although feedforward design has improved significantly with respect to traditional acceleration feedforward, a snap feedforward with systematic tuning for position-dependent compliance, capable of estimation at any arbitrary position, is currently lacking. This paper models the snap feedforward parameter as a continuous function of position by means of a Gaussian Process (GP) [1]}, [2]}, which allows for the compensation of position-dependent compliance without an LPV model. In addition, a GP is non-parametric and therefore does not require an assumption on the parametric form between the position and the snap feedforward parameter. In this paper, the feedforward parameters of a system are learned in a trial-to-trial fashion using ILC with Basis Functions (ILCBF) [3]}. The contributions include:
